It is evident that computing devices, once connected by a network, are particularly useful for enabling communication between individuals. Various technologies have been introduced to facilitate the transmission of messages between networked computing devices and the users thereof. One such technology that is increasingly popular is the use of online social networks, or OSNs. In an OSN, users of networked computing devices participate in the OSN to transmit messages to and receive messages from other users in one-to-one and one-to-many formats.
The ability to efficiently disseminate messages that is provided by online social networks is revolutionizing the way people communicate using networked computing devices. Influential participants are able to use online social networks to spread information with unprecedented speed and reach. However, with massive numbers of participants and massive amounts of data being generated by participant activity (the so-called Big Data problem), a particular technical problem arises in that it is difficult to automatically identify which participants are the influential participants who are able to efficiently spread messages within the online social networks, particularly with respect to messages relating to a given topic.
The question of automatic identification of influential participants on OSNs has received widespread attention in the recent years. One technique promoted by many OSNs as a measure of the influence of a given participant is to count the number of followers he or she has accumulated (that is, the number of participants that will receive messages transmitted by the given participant). This metric has been shown to be inadequate, because participants who have a large number of followers are not necessarily influential in terms of spawning additional messages, such as retweets or mentions, by those followers. As such, participants who have a large number of followers who are unlikely to re-share messages may not have as much actual influence as participants who have fewer followers but who are more likely to re-share messages.
Another technique considered for the identification of influential participants on OSNs is the PageRank algorithm. The PageRank algorithm has been used to address the general problem of identification of influential nodes on a network graph by relating the importance of a node to the stationary distribution of a random walk on the given network. The PageRank algorithm and its variants have thus been used to attempt to identify influential participants on OSNs, such as Twitter, based on the graph topology of the OSN. This technique is inferior, because the probability transition matrix of the PageRank technique depends entirely on the graph topology, while interaction activity is completely ignored. Further, the choice of a teleportation constant for use in PageRank may work well for determining relevance of webpages, but has no substantial basis when applied to OSNs. The same is true for ranking influential participants in an OSN based on a personalized PageRank.
Another technique for finding influential participants of OSNs finds a minimal set of seed participants which, when activated, lead to the maximum number of activated participants on the network. However, such techniques are based on time-consuming Monte Carlo analysis, and therefore are unsuitable for finding influential participants of large, real-world OSNs.
What is desired is an improvement in the ability of users to utilize networked computing device technology to broadly disseminate information via OSNs by identifying influential participants based on the actual activity interactions between participants for a given set of topics.